Category Archives: social networks

There is an interesting article by Robin Dunbar in The New Scientist: Dunbar’s Number was named after Robin, from his theorizing that humans only had the brain capacity to manage roughly 150 relationships, although depending on gender, social skills and personality, this number could vary from 100-250. Dunbar observes that communication often breaks down when one exceeds 150 individuals (as evidenced in the Crimean War by the Charge of the Light Brigade) and the modern military and businesses only exceed these limits through strict hierarchies.

Dunbar theorizes that language, laughter and communal music-making evolved as a way to stay connected to a larger group of individuals than possible through physical acts like grooming. Dunbar: “[N]ot only can we speak to many people at the same time, we can also exchange information about the state of our networks in a way that other primates cannot. Gossip, I have argued, is a very human form of grooming.” Christakis and Fowler (in the excellent book Connected) note that “…language is a less yucky and more efficient way to get to know our peers since we can talk to several friends at once but only groom them one at a time. In fact, in a conversation with a small group, we can assess the behavior, health, aggressiveness, and altruism of several individuals simultaneously. Plus, we can talk to someone else while engaged in another activity, like foraging for food in a refrigerator.” Christakis and Fowler note how radical the idea is that language evolved not primarily as a way to exchange information but to maintain group cohesion. “Dunbar estimates that language would have to be 2.8 times more efficient than grooming in order to sustain the [average] group size seen in humans” (one speaker per 2.8 listeners).

While language may have originally evolved, as per Dunbar, to maintain a slightly larger group size, once developed it was in principle possible to use language to maintain social relations on a tribal or national level.

A few other excerpts from Dunbar’s article:

Group living needn’t tax your intelligence too much. In a loose herd, cues such as body size or aggressiveness may be enough to judge whether you should challenge or steer clear of another individual. In bonded networks, however, you need to know each member’s personal characteristics and those of the friends and relations that might come to their aid. Keeping track of the ever-changing web of social relationships requires considerable mental computing power.

As a reflection of this, there is a correlation between the size of a species’ brain– in particular its neocortex– and the typical size of its social groups. In other words, brain size seems to place a limit on the number of relationships an individual can have. This link between group size and brain size is found in primates and perhaps a handful of other mammals that form bonded societies such as dolphins, dogs, horses and elephants. In all other mammals and birds, unusually large brains are found only in species that live in pair-bonded (monogamous) social groups.

As group size increases so too does the number of relationships that need servicing. Social effort is not spread evenly. Individuals put most effort into their closest relationships to ensure that these friends will help out when they need them. At the same time they maintain the coherence of the group. As a result, social networks resemble a nested hierarchy with two or three best friends linked into larger groupings of more casual friends, and weaker relationships bonding the entire group. This hierarchy typically has a scaling ratio of three– each layer of decreasing intimacy is three times larger than the one before it….

HUMAN SOCIAL NETWORKS

Our social networks can have dramatic effects on our lives. Your chances of becoming obese, giving up smoking, being happy or depressed, or getting divorced are all influenced by how many of your close friends do these things. A good social network could even help you live longer since laughing with friends triggers the release of endorphins, which seem to “tune” the immune system, making you more resilient to disease. So what factors influence the form and function that our social networks take.

In traditional societies, everyone in the community is related to everyone else, either as biological relatives or in-laws. In post-industrial societies this is no longer true– we live among strangers, some of whom become friends. As a result, our social circles really consist of two separate networks– family and friends– with roughly half drawn from each group.

Because the pull of kinship is so strong, we give priority to family, choosing to include them in our networks above unrelated individuals. Indeed, people who come from large extended families actually have fewer friends. One reason we favour kin is that they are much more likely to come to our aid when we need help than unrelated individuals, even if these are very good friends.

Family and friend relationships differ in other important ways, too. One is that friendships are very prone to decay if untended. Failure to see a friend for six months or so leaves us feeling less emotionally attached to them, causing them to drop down through the layers of our network hierarchy. Family relationships, by contrast, are incredibly resilient to neglect. As a result, the family half of our network remains constant throughout most of our lives whereas the friendship component undergoes considerable change over time, with up to 20 per cent turnover every few years.

“Low social interaction as high a risk factor for early death as smoking 15 cigarettes daily or being an alcoholic, and twice the risk factor of obesity.”

Julianne Holt-Lunstad, a psychologist at BYU, published a recent meta-analysis with Timothy Smith and J. Bradley Layton (that culls from learning across 148 longitudinal health studies covering over 300,000 individuals). They showed that increased involvement in social networks on average reduces one’s chance of mortality over the period of any particular study by 50+%, a greater effect than either stopping smoking or eliminating one’s obesity/physical inactivity.

The study “Social Relationships and Mortality Risk: A Meta-analytic Review” appears in the journal PLoS Medicine. They controlled for baseline health status, and found consistent results for friendships with family, friends, neighbors and colleagues across age, gender, initial health status, cause of death, and follow-up period.

The life-protective benefits of friendship were strongest for complex measures of social integration and lowest for simple measures of residential status (e.g., living alone versus with others) . In studies that had greater dimensions of social involvement (whether one was in a network, the kinds of social support one got, etc.), the life-protecting benefits of friendships were higher, likely corresponding to the multiple pathways through which friendships provide benefits.

Low social interaction, according to the authors, was as high a risk factor for early death as smoking 15 cigarettes a day or being an alcoholic. Low social interaction was a higher risk factor than not exercising and twice as high a risk factor for early death as obesity.

Co-author Tim Smith noted: “We take relationships for granted as humans – we’re like fish that don’t notice the water….That constant interaction is not only beneficial psychologically but directly to our physical health.”

The longitudinal studies they analyzed tracked health outcomes and social interaction for a period of seven and a half years on average.

The 50% increased survival rate is quite likely an underestimate: these longitudinal studies don’t track relationship quality but only one’s inclusion in a social network, so they include negative relationships as well. Survival benefits of friendships are likely to be much higher if one could isolate only positive and healthy social relationships.

Holt-Lunstad speculated that the pathways of social relationships to improved longevity stem range from “a calming touch to finding meaning in life.” She believes that those who are socially connected take greater responsibility for others’ and their own lives and take fewer risks.

Unlike some other work, such as Eric Klinenberg’s Heat Wave, where shut-in elderly were especially at risk of death in Chicago’s 1995 heat wave, the findings of Holt-Lunstad are generalizable to all age groups.

Mark Granovetter is famous for uncovering the strength of weak ties in job searches (i.e., that weaker ties ironically are more helpful in landing jobs than one’s close friends). Granoveter, after interviewing job seekers, posited that it was because one’s close friends tie one back to jobs and job leads that one already knew about whereas weak ties connected one to jobs that one hadn’t heard of.

Sandra Smith, sociology at Berkeley, is doing interesting work uncovering the why. She’s interviewed 157 workers of various races and various job levels at a public university (Berkeley?) to learn of cases where they did and didn’t help people land jobs and what was good or bad about the experience. Smith notes that in Granovetter’s work the job seekers often don’t know exactly what or was not done by their strong or weak tie. [Her past work has been on how distrust hurts low-income blacks in the job referral process, but this new work, as of yet unpublished, is more general.]

It turns out, that people generally don’t refer their close friends to jobs for two reasons: 1) they are more worried that it will reflect badly on them if it doesn’t work out; and 2) they are more likely to know of the warts and foibles of their close friends and believe these could interfere with being a good worker (e.g., Jim stays up late to watch sports, or Charles has too much of an attitude, or Jane is too involved with her sick father). Weak friends one can more easily project good attributes onto and believe this will work out.

She spoke of one interesting case, “Redmond”, who worked in a growing university department that was hiring 30 new people and whose manager asked workers to help refer good employees. Redmond was asked soon thereafter by the parking attendant at his church whether he knew of any jobs for his wife who had lost her job (both the parking attendant and his wife were Ethiopian immigrants in the US and lived at Redmond’s church). Redmond barely knew either of them, but took many steps to advance her candidacy (driving her to the interview, introducing her to people at the office, checking on her candidacy, and getting information filled out again when the paperwork was lost, etc.). Redmond also had 10-15 good friends who needed a job, but he only told 2 about the available jobs, and even for those 2, didn’t take any steps to advance their candidacy since he had reservations about them.

In some cases, people did intervene on behalf of family or friends, but sometimes this was more lukewarm (e.g., enabling their applicant-friend to put the job-holder’s name on the applicant as a referrer, but making no efforts behind the scenes to advance their candidacy).

The job holders seem to put the interests of the workplace generally ahead of the interests of their friends, perhaps because they are jealously guarding their workplace reputation would could be sullied by a poor referral. The job holders act as “moral” gatekeepers, trying to keep out the unworthy.

Smith is working to try to categorize types of job assistance and what leads one to help a friend/relative vs. helping a weak tie, and whether this assistance is to help the friend or improve the workplace.

James Panichi: So Generation X is less involved socially than the baby boomers before it?

Robert Putnam: That’s right. Now of course that’s not the end of the story, and in fact that generational engine which has been running to kind of drive American social capital down for 30 or 40 years, actually recently reversed and so actually I’m a little more optimistic right now. But when I wrote Bowling Alone that engine of generational arithmetic, every year the most civically engaged Americans leaving the population by death, adding another slice of people at the bottom of the age are people who are much less civically engaged, that was inexorably driving down various measures of social connection….” [See “Still Bowling Alone?“]

James Panichi: Is there a dark side to social capital?…[L]let me give you an Australian example. There are the old school networks of people who’ve been to private schools; there’s Masonic Lodges, there are social clubs which the old establishment social clubs in both Melbourne and Sydney which are more or less anti-semitic, I mean there are real institutions which a lot of Australians would have problems with, and who they would say, ‘Look this is an example of social capital that is not necessarily good, it’s about people doing deals behind closed doors’.

Robert Putnam: I don’t disagree with that at all. I don’t disagree with that at all, I mean after all, I’ve not said all networks are good, I just said networks are very powerful and they can have powerful positive effects and powerful negative effects. But all the examples you used of what I would call bonding social capital, and this is a very clear distinction made in the literature, bonding social capital refers to my ties to people like me, so my ties to other white, elderly, male, professors, that’s my bonding social capital, and bridging social capital are my ties to people unlike me, to people of a different generation, race, a different religion, different ethnicity, I’m not saying always bridging good, bonding bad, but in general examples that you used are negatively used social capital; social capital is used to the detriment of other people, are mostly bonding social capital within the upper class, and one of the things we’re currently working on actually in America, is the apparent discovery that while social capital is rising among kids from upper middle class backgrounds, my grandchildren are connected… but they’re connected with other people and they’re dressed for success, they’re going to do just fine. But our research shows that working class kids or kids from lower classes, white and black, this is not a matter of race, kids from lower class backgrounds, increasingly in America, are isolated, they’re less likely to go to church than working class kids used to, they’re less likely to belong to organisations like the Scouts than working class kids used to be. They spend less time with their parents, they have fewer friends at school, they’re much lower in social trust, trust in their environment, they are in short, increasingly socially isolated. Actually that’s the problem here that I’m most concerned about at the moment, because I think after 9/11 there was kind of a burst of social capital, or interest in civic life among American young people. I think the basic Bowling Alone trend has now begun to turn, but in a way it’s begun to turn in the worst possible way in the sense that it’s the upper class kids from upper class backgrounds who are more connected and working class kids are really left entirely on their own, and that’s a serious problem.

Listen to Robert Putnam interview with James Panichi on the “National Interest” ABC Radio International “Healthy, wealthy and happy“

Scholars at Oxford have refuted the notion that all mammals over time developed larger brains. Instead Dr. Susanne Shultz and Prof. Robin Dunbar found over a span of 60 million years that more social creatures, among them humans, had the most rapidly expanding brain sizes to cope with the complexity of collaboration, social norms and coordination.

“The research team analysed available data on the brain size and body size of more than 500 species of living and fossilised mammals. It found that the brains of monkeys grew the most over time, followed by horses, dolphins, camels and dogs. The study shows that groups of mammals with relatively bigger brains tend to live in stable social groups. The brains of more solitary mammals, such as cats, deer and rhino, grew much more slowly during the same period.”

They noted that the fact that cats’ brains did not expand while dogs’ and horses’ brains did, can be accounted for by the far more solitary lives that cats lead in relation to dogs and horses, which interact far more with humans.

Obviously, since these evolutionary anthropologists couldn’t go back in time to distinguish social cavemen from more solitary cavemen, it is impossible to tell whether the expansion of brain size was related to the average levels of socialization of a species or whether this same pattern would have held true at the individual level: with offspring of more social parents having larger brains than offspring of less social humans.

Nonetheless, food for thought… The implication: get out and socialize and help our species to continue to grow our average brain size, although the results may not be noticeable within your lifetime.

The Wall Street Journal recently noted how insurance companies (Aviva PLC, Prudential Financial, AIG) bet on whom to insure at what rates through data mining. Much of the info gleaned from online purchases and other digital traces is more lifestyle: is the insurance applicant an athlete? a TV addict? a hunter?

But some of the information is social capital-related:

Increasingly, some gather online information, including from social-networking sites. Acxiom Corp., one of the biggest data firms, says it acquires a limited amount of “public” information from social-networking sites, helping “our clients to identify active social-media users, their favorite networks, how socially active they are versus the norm, and on what kind of fan pages they participate.”

For insurers and data-sellers alike, the new techniques could open up a regulatory can of worms. The information sold by marketing-database firms is lightly regulated. But using it in the life-insurance application process would “raise questions” about whether the data would be subject to the federal Fair Credit Reporting Act, says Rebecca Kuehn of the Federal Trade Commission’s division of privacy and identity protection. The law’s provisions kick in when “adverse action” is taken against a person, such as a decision to deny insurance or increase rates. The law requires that people be notified of any adverse action and be allowed to dispute the accuracy or completeness of data, according to the FTC.

The article also notes that Celent, an insurance consulting division of Marsh & McLennan, indicates that such online social-network data could be mined for policing fraud and in making pricing decisions: “A life insurer might want to scrutinize an applicant who reports no family history of cancer, but indicates online an affinity with a cancer-research group, says Mike Fitzgerald, a Celent senior analyst. ‘Whether people actually realize it or not, they are significantly increasing their personal transparency,’ he says. ‘It’s all public, and it’s electronically mineable.’ ”

We applaud the life insurers for coming to the late realization that social capital data is strongly related to health, but strongly believe they should be more transparent about what they are doing. Then it wouldn’t violate privacy concerns and it would have the added benefit of making the insured better aware of the positive health impact of being more involved civicly and socially, which might actually induce those who are less engaged to become more so.

Nick Christakis and James Fowler (whose research we’ve previously highlighted) is back with research that shows how one can easily use “sensors” in a network to track and get early warning regarding the spread of epidemics.

They took advantage of the “friendship paradox” to do so. In any real-life network, our friends are more popular than we are. [This is true mathematically in any group with some loners and some social butterflies. If you poll members in the group about their friendships, far more of those friends who are reported are going to be the social butterflies. If far more people reported friendships with the loners, they wouldn’t be loners. See discussion here.]

Thus by asking random people in a network, in this case Harvard students, about their friends, researchers know that their friends are more centrally located in these networks. Then one can track behavior among the random group and their friends, in this case the spread of H1N1 flu (swine flu) among 744 Harvard students in 2009.

Those more central in these networks (the “friend” group) got the flu a full 16-47 days earlier than the random group. Thus, for public authorities, monitoring such a “friend” group could give one early indication of a spreading epidemic; they could serve as “canaries in the coal mine”. If the process of spreading was person-to-person rather than being exposed to some impersonal information (via a website or a broadcast), one could also track the difference between a random group and a friend group to predict other more positive epidemics, like the spread of information, or the diffusion of a product, or a social norm.

We write in general on this blog about the positive benefits of social ties (social capital), but Fowler and Christakis’ study also shows you that having friends and being centrally located has its costs: in this case getting the flu faster. [In some ways, this is analogous to Gladwell’s discussion in the Tipping Point of how Mavens, Connectors and Salesmen may be disproportionately influential in the spread of ideas through networks, although Fowler and Christakis are far more mathematical in identifying who these central folks are.]

The “friends group manifested the flu roughly two weeks prior to the random group using one method of detection, and a full 46 days prior to the epidemic peak using another method.

‘We think this may have significant implications for public health,’ said Christakis. ‘Public health officials often track epidemics by following random samples of people or monitoring people after they get sick. But that approach only provides a snapshot of what’s currently happening. By simply asking members of the random group to name friends, and then tracking and comparing both groups, we can predict epidemics before they strike the population at large. This would allow an earlier, more vigorous, and more effective response.’

‘If you want a crystal ball for finding out which parts of the country are going to get the flu first, then this may be the most effective method we have now,’ said Fowler. ‘Currently used methods are based on statistics that lag the real world – or, at best, are contemporaneous with it. We show a way you can get ahead of an epidemic of flu, or potentially anything else that spreads in networks.’

Christakis also notes that if you provided a random 30% in a population with immunity to a flu, you don’t protect the greater public, but if you took a random 30% of the population, asked them to name their friends, and then provided immunization to their friends, in a typical network the “friend” immunization strategy would achieve as high immunity protection for the entire network as giving 96% of the population immunity shots, but at less than 1/3 the cost.

The following video shows how the nodes that light up first (markers for getting the flu) are more central and far less likely to be at the periphery of the social network. The red dots are people getting the flu; the yellow dots are friends of people with the flu and the size of the dot is proportional to how many of their friends have the flu.

Good summary of this research and its implications here: Nick Christakis TED talk (June 2010) – How social networks predict spread of flu. Nick also discusses some of the implications of computational social science, which we’ve previously discussed here under the heading of digital traces. Nick discusses how one could use data gathered from these networks (either passively or actively) to do things like predict recessions from patterns of fuel consumption by truckers, to communicate with drivers of a road of impending traffic jams ahead of them (by monitoring from cell phone users on the road ahead of them how rapidly they are changing cell phone towers) to asking those central in a mobile cellphone network (easily mapable today) to text their daily temperature (to monitor for impending flu epidemics). Obviously these raise issues of privacy, which Nick does not discuss.

Nick Christakis presenting a talk at TED — The Hidden Influence of Social Networks. (February 2010). In the talk he notes that while almost half of the variation in our number of friends is genetically-based (46%), that another equally large portion (47%) of whether your friends know each other is a function of whether your friends are the type that introduce (“knit”) their friends together or keep them apart (what they call “transitivity”). About a third of whether you are in the center of social networks or not is genetically inherited. Christakis believes that these social networks are critically important to transmitting ideas, and kindness, and information and goodness; and if society realized how valuable these networks were, we’d focus far more of our time, energy and resources into helping these networks to flourish.